Pandas Profiling Report

Dataset statistics

Number of variables	15
Number of observations	31595
Missing cells	72
Missing cells (%)	< 0.1%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	3.6 MiB
Average record size in memory	120.0 B

Variable types

DateTime	2
Numeric	8
Categorical	5

Warnings

`Previsión diaria D+1 fotovoltaica` has 13393 (42.4%) zeros	Zeros
`Weekday` has 4512 (14.3%) zeros	Zeros
`Hour` has 1317 (4.2%) zeros	Zeros

Reproduction

Analysis started	2022-08-11 17:08:29.965775
Analysis finished	2022-08-11 17:08:45.441704
Duration	15.48 seconds
Software version	pandas-profiling v2.11.0
Download configuration	config.yaml

DateIndex
Date

Distinct	31592
Distinct (%)	> 99.9%
Missing	0
Missing (%)	0.0%
Memory size	247.0 KiB

Minimum	2019-01-01 00:00:00
Maximum	2022-08-09 11:00:00

Histogram

Histogram with fixed size bins (bins=50)

Previsión diaria D+1 demanda
Real number (ℝ_≥0)

Distinct	15587
Distinct (%)	49.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	27796.52349
Minimum	14170
Maximum	41773
Zeros	0
Zeros (%)	0.0%
Memory size	247.0 KiB

Quantile statistics

Minimum	14170
5-th percentile	20833
Q1	24100.5
median	27837
Q3	31234
95-th percentile	35065.9
Maximum	41773
Range	27603
Interquartile range (IQR)	7133.5

Descriptive statistics

Standard deviation	4494.007319
Coefficient of variation (CV)	0.1616751577
Kurtosis	-0.7678018411
Mean	27796.52349
Median Absolute Deviation (MAD)	3573
Skewness	0.07802011851
Sum	878231159.6
Variance	20196101.78
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
30999	9	< 0.1%
31446	9	< 0.1%
31126	9	< 0.1%
31189	9	< 0.1%
24553	9	< 0.1%
23522	8	< 0.1%
23621	8	< 0.1%
31151	8	< 0.1%
27439	8	< 0.1%
24861	8	< 0.1%
Other values (15577)	31510	99.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
14170	1	< 0.1%
14329	1	< 0.1%
14927	1	< 0.1%
14975	1	< 0.1%
15176	1	< 0.1%
15529	1	< 0.1%
15724	1	< 0.1%
16231	1	< 0.1%
16246	1	< 0.1%
16303	1	< 0.1%

Value	Count	Frequency (%)
41773	1	< 0.1%
40987	1	< 0.1%
40599	1	< 0.1%
40589	1	< 0.1%
40514	1	< 0.1%
40459	1	< 0.1%
40406	2	< 0.1%
40307	1	< 0.1%
40262	1	< 0.1%
40168	1	< 0.1%

Previsión diaria D+1 fotovoltaica
Real number (ℝ_≥0)

ZEROS

Distinct	15359
Distinct (%)	48.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	1988.770945
Minimum	0
Maximum	11892
Zeros	13393
Zeros (%)	42.4%
Memory size	247.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	144
Q3	3419.6
95-th percentile	7956.22
Maximum	11892
Range	11892
Interquartile range (IQR)	3419.6

Descriptive statistics

Standard deviation	2782.867091
Coefficient of variation (CV)	1.399289897
Kurtosis	0.9147956786
Mean	1988.770945
Median Absolute Deviation (MAD)	144
Skewness	1.359970669
Sum	62835218
Variance	7744349.247
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	13393	42.4%
0.1	75	0.2%
0.3	58	0.2%
0.2	55	0.2%
0.4	33	0.1%
0.6	31	0.1%
1.1	29	0.1%
0.8	25	0.1%
0.9	23	0.1%
0.5	23	0.1%
Other values (15349)	17850	56.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	13393	42.4%
0.1	75	0.2%
0.2	55	0.2%
0.3	58	0.2%
0.4	33	0.1%
0.5	23	0.1%
0.6	31	0.1%
0.7	15	< 0.1%
0.8	25	0.1%
0.9	23	0.1%

Value	Count	Frequency (%)
11892	1	< 0.1%
11874.9	1	< 0.1%
11857.3	1	< 0.1%
11823.5	1	< 0.1%
11811.2	1	< 0.1%
11777.6	1	< 0.1%
11773	1	< 0.1%
11760.4	1	< 0.1%
11759.4	1	< 0.1%
11754.6	1	< 0.1%

Previsión diaria D+1 eólica
Real number (ℝ_≥0)

Distinct	13196
Distinct (%)	41.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	6315.756224
Minimum	348
Maximum	19109
Zeros	0
Zeros (%)	0.0%
Memory size	247.0 KiB

Quantile statistics

Minimum	348
5-th percentile	1743.7
Q1	3542
median	5597
Q3	8471.5
95-th percentile	13467.3
Maximum	19109
Range	18761
Interquartile range (IQR)	4929.5

Descriptive statistics

Standard deviation	3569.313064
Coefficient of variation (CV)	0.5651442104
Kurtosis	0.1266514708
Mean	6315.756224
Median Absolute Deviation (MAD)	2331
Skewness	0.8118129128
Sum	199546317.9
Variance	12739995.75
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5106	13	< 0.1%
3690	11	< 0.1%
3293	11	< 0.1%
3807	11	< 0.1%
3393	11	< 0.1%
3550	11	< 0.1%
2892	10	< 0.1%
3965	10	< 0.1%
5597	10	< 0.1%
6046	10	< 0.1%
Other values (13186)	31487	99.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
348	1	< 0.1%
380	1	< 0.1%
386	1	< 0.1%
401	1	< 0.1%
403	1	< 0.1%
427	1	< 0.1%
429	1	< 0.1%
432	1	< 0.1%
445	1	< 0.1%
453	1	< 0.1%

Value	Count	Frequency (%)
19109	1	< 0.1%
18965	1	< 0.1%
18921	1	< 0.1%
18784	1	< 0.1%
18712	1	< 0.1%
18635	1	< 0.1%
18617	1	< 0.1%
18573	1	< 0.1%
18567	1	< 0.1%
18491	1	< 0.1%

Precio mercado SPOT Diario
Real number (ℝ_≥0)

Distinct	11504
Distinct (%)	36.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	86.2803874
Minimum	0.01
Maximum	700
Zeros	0
Zeros (%)	0.0%
Memory size	247.0 KiB

Quantile statistics

Minimum	0.01
5-th percentile	19
Q1	38.5
median	52.17
Q3	115.965
95-th percentile	239.551
Maximum	700
Range	699.99
Interquartile range (IQR)	77.465

Descriptive statistics

Standard deviation	75.36899787
Coefficient of variation (CV)	0.8735356915
Kurtosis	3.123851958
Mean	86.2803874
Median Absolute Deviation (MAD)	20.83
Skewness	1.663693752
Sum	2726028.84
Variance	5680.48584
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1.95	108	0.3%
50	92	0.3%
40	88	0.3%
30	83	0.3%
45	68	0.2%
25	61	0.2%
35	59	0.2%
200	51	0.2%
55	50	0.2%
49	50	0.2%
Other values (11494)	30885	97.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.01	2	< 0.1%
0.03	2	< 0.1%
0.1	3	< 0.1%
0.15	1	< 0.1%
0.16	25	0.1%
0.2	3	< 0.1%
0.32	1	< 0.1%
0.44	3	< 0.1%
0.5	2	< 0.1%
0.51	3	< 0.1%

Value	Count	Frequency (%)
700	1	< 0.1%
654.91	1	< 0.1%
651	2	< 0.1%
650	1	< 0.1%
645	1	< 0.1%
605	1	< 0.1%
603.08	1	< 0.1%
601	1	< 0.1%
600	2	< 0.1%
582	1	< 0.1%

Signo del desvío
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.8 MiB

-1.0	16178
1.0	15417

Length

Max length	4
Median length	4
Mean length	3.512043045
Min length	3

Characters and Unicode

Total characters	110963
Distinct characters	4
Distinct categories	3 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	-1.0
2nd row	-1.0
3rd row	-1.0
4th row	-1.0
5th row	-1.0

Value	Count	Frequency (%)
-1.0	16178	51.2%
1.0	15417	48.8%

Histogram of lengths of the category

Value	Count	Frequency (%)
1.0	31595	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	31595	28.5%
.	31595	28.5%
0	31595	28.5%
-	16178	14.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	63190	56.9%
Other Punctuation	31595	28.5%
Dash Punctuation	16178	14.6%

Most frequent character per category

Value	Count	Frequency (%)
1	31595	50.0%
0	31595	50.0%

Value	Count	Frequency (%)
-	16178	100.0%

Value	Count	Frequency (%)
.	31595	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	110963	100.0%

Most frequent character per script

Value	Count	Frequency (%)
1	31595	28.5%
.	31595	28.5%
0	31595	28.5%
-	16178	14.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	110963	100.0%

Most frequent character per block

Value	Count	Frequency (%)
1	31595	28.5%
.	31595	28.5%
0	31595	28.5%
-	16178	14.6%

Signo_lag_24h
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	24
Missing (%)	0.1%
Memory size	1.8 MiB

-1.0	16172
1.0	15399

Length

Max length	4
Median length	4
Mean length	3.512242248
Min length	3

Characters and Unicode

Total characters	110885
Distinct characters	4
Distinct categories	3 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	-1.0
2nd row	-1.0
3rd row	-1.0
4th row	-1.0
5th row	-1.0

Value	Count	Frequency (%)
-1.0	16172	51.2%
1.0	15399	48.7%
(Missing)	24	0.1%

Histogram of lengths of the category

Value	Count	Frequency (%)
1.0	31571	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	31571	28.5%
.	31571	28.5%
0	31571	28.5%
-	16172	14.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	63142	56.9%
Other Punctuation	31571	28.5%
Dash Punctuation	16172	14.6%

Most frequent character per category

Value	Count	Frequency (%)
1	31571	50.0%
0	31571	50.0%

Value	Count	Frequency (%)
-	16172	100.0%

Value	Count	Frequency (%)
.	31571	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	110885	100.0%

Most frequent character per script

Value	Count	Frequency (%)
1	31571	28.5%
.	31571	28.5%
0	31571	28.5%
-	16172	14.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	110885	100.0%

Most frequent character per block

Value	Count	Frequency (%)
1	31571	28.5%
.	31571	28.5%
0	31571	28.5%
-	16172	14.6%

Signo_lag_48h
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	48
Missing (%)	0.2%
Memory size	1.8 MiB

-1.0	16165
1.0	15382

Length

Max length	4
Median length	4
Mean length	3.512410055
Min length	3

Characters and Unicode

Total characters	110806
Distinct characters	4
Distinct categories	3 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	-1.0
2nd row	-1.0
3rd row	-1.0
4th row	-1.0
5th row	-1.0

Value	Count	Frequency (%)
-1.0	16165	51.2%
1.0	15382	48.7%
(Missing)	48	0.2%

Histogram of lengths of the category

Value	Count	Frequency (%)
1.0	31547	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	31547	28.5%
.	31547	28.5%
0	31547	28.5%
-	16165	14.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	63094	56.9%
Other Punctuation	31547	28.5%
Dash Punctuation	16165	14.6%

Most frequent character per category

Value	Count	Frequency (%)
1	31547	50.0%
0	31547	50.0%

Value	Count	Frequency (%)
-	16165	100.0%

Value	Count	Frequency (%)
.	31547	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	110806	100.0%

Most frequent character per script

Value	Count	Frequency (%)
1	31547	28.5%
.	31547	28.5%
0	31547	28.5%
-	16165	14.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	110806	100.0%

Most frequent character per block

Value	Count	Frequency (%)
1	31547	28.5%
.	31547	28.5%
0	31547	28.5%
-	16165	14.6%

Holiday
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.7 MiB

0	30875
1	720

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	31595
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Value	Count	Frequency (%)
0	30875	97.7%
1	720	2.3%

Histogram of lengths of the category

Value	Count	Frequency (%)
0	30875	97.7%
1	720	2.3%

Most occurring characters

Value	Count	Frequency (%)
0	30875	97.7%
1	720	2.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	31595	100.0%

Most frequent character per category

Value	Count	Frequency (%)
0	30875	97.7%
1	720	2.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	31595	100.0%

Most frequent character per script

Value	Count	Frequency (%)
0	30875	97.7%
1	720	2.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	31595	100.0%

Most frequent character per block

Value	Count	Frequency (%)
0	30875	97.7%
1	720	2.3%

Year
Categorical

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.8 MiB

2020	8784
2019	8760
2021	8760
2022	5291

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	126380
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2019
2nd row	2019
3rd row	2019
4th row	2019
5th row	2019

Value	Count	Frequency (%)
2020	8784	27.8%
2019	8760	27.7%
2021	8760	27.7%
2022	5291	16.7%

Histogram of lengths of the category

Value	Count	Frequency (%)
2020	8784	27.8%
2021	8760	27.7%
2019	8760	27.7%
2022	5291	16.7%

Most occurring characters

Value	Count	Frequency (%)
2	59721	47.3%
0	40379	32.0%
1	17520	13.9%
9	8760	6.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	126380	100.0%

Most frequent character per category

Value	Count	Frequency (%)
2	59721	47.3%
0	40379	32.0%
1	17520	13.9%
9	8760	6.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	126380	100.0%

Most frequent character per script

Value	Count	Frequency (%)
2	59721	47.3%
0	40379	32.0%
1	17520	13.9%
9	8760	6.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	126380	100.0%

Most frequent character per block

Value	Count	Frequency (%)
2	59721	47.3%
0	40379	32.0%
1	17520	13.9%
9	8760	6.9%

Month
Real number (ℝ_≥0)

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	6.129134357
Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Memory size	247.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	3
median	6
Q3	9
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.37924439
Coefficient of variation (CV)	0.5513412161
Kurtosis	-1.120129987
Mean	6.129134357
Median Absolute Deviation (MAD)	3
Skewness	0.1511555164
Sum	193650
Variance	11.41929264
Monotocity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
7	2976	9.4%
5	2976	9.4%
1	2976	9.4%
3	2972	9.4%
6	2880	9.1%
4	2880	9.1%
2	2712	8.6%
8	2436	7.7%
10	2235	7.1%
12	2232	7.1%
Other values (2)	4320	13.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	2976	9.4%
2	2712	8.6%
3	2972	9.4%
4	2880	9.1%
5	2976	9.4%
6	2880	9.1%
7	2976	9.4%
8	2436	7.7%
9	2160	6.8%
10	2235	7.1%

Value	Count	Frequency (%)
12	2232	7.1%
11	2160	6.8%
10	2235	7.1%
9	2160	6.8%
8	2436	7.7%
7	2976	9.4%
6	2880	9.1%
5	2976	9.4%
4	2880	9.1%
3	2972	9.4%

Day
Real number (ℝ_≥0)

Distinct	31
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	15.64969141
Minimum	1
Maximum	31
Zeros	0
Zeros (%)	0.0%
Memory size	247.0 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	8
median	16
Q3	23
95-th percentile	29
Maximum	31
Range	30
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	8.8133257
Coefficient of variation (CV)	0.5631629066
Kurtosis	-1.199056187
Mean	15.64969141
Median Absolute Deviation (MAD)	8
Skewness	0.01695488994
Sum	494452
Variance	77.6747099
Monotocity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
8	1056	3.3%
7	1056	3.3%
1	1056	3.3%
2	1056	3.3%
3	1056	3.3%
4	1056	3.3%
5	1056	3.3%
6	1056	3.3%
9	1044	3.3%
25	1033	3.3%
Other values (21)	21070	66.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1056	3.3%
2	1056	3.3%
3	1056	3.3%
4	1056	3.3%
5	1056	3.3%
6	1056	3.3%
7	1056	3.3%
8	1056	3.3%
9	1044	3.3%
10	1032	3.3%

Value	Count	Frequency (%)
31	600	1.9%
30	936	3.0%
29	959	3.0%
28	1031	3.3%
27	1032	3.3%
26	1032	3.3%
25	1033	3.3%
24	1032	3.3%
23	1032	3.3%
22	1032	3.3%

Weekday
Real number (ℝ_≥0)

ZEROS

Distinct	7
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	2.999145434
Minimum	0
Maximum	6
Zeros	4512
Zeros (%)	14.3%
Memory size	247.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	3
Q3	5
95-th percentile	6
Maximum	6
Range	6
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	1.999991905
Coefficient of variation (CV)	0.666853925
Kurtosis	-1.250258297
Mean	2.999145434
Median Absolute Deviation (MAD)	2
Skewness	0.000795255058
Sum	94758
Variance	3.999967618
Monotocity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	4524	14.3%
5	4512	14.3%
4	4512	14.3%
3	4512	14.3%
2	4512	14.3%
0	4512	14.3%
6	4511	14.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	4512	14.3%
1	4524	14.3%
2	4512	14.3%
3	4512	14.3%
4	4512	14.3%
5	4512	14.3%
6	4511	14.3%

Value	Count	Frequency (%)
6	4511	14.3%
5	4512	14.3%
4	4512	14.3%
3	4512	14.3%
2	4512	14.3%
1	4524	14.3%
0	4512	14.3%

Hour
Real number (ℝ_≥0)

ZEROS

Distinct	24
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	11.49802184
Minimum	0
Maximum	23
Zeros	1317
Zeros (%)	4.2%
Memory size	247.0 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	5
median	11
Q3	17
95-th percentile	22
Maximum	23
Range	23
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	6.922199035
Coefficient of variation (CV)	0.6020339091
Kurtosis	-1.204129259
Mean	11.49802184
Median Absolute Deviation (MAD)	6
Skewness	0.0004461886648
Sum	363280
Variance	47.91683947
Monotocity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
0	1317	4.2%
1	1317	4.2%
11	1317	4.2%
10	1317	4.2%
9	1317	4.2%
8	1317	4.2%
7	1317	4.2%
6	1317	4.2%
5	1317	4.2%
3	1317	4.2%
Other values (14)	18425	58.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1317	4.2%
1	1317	4.2%
2	1316	4.2%
3	1317	4.2%
4	1317	4.2%
5	1317	4.2%
6	1317	4.2%
7	1317	4.2%
8	1317	4.2%
9	1317	4.2%

Value	Count	Frequency (%)
23	1316	4.2%
22	1316	4.2%
21	1316	4.2%
20	1316	4.2%
19	1316	4.2%
18	1316	4.2%
17	1316	4.2%
16	1316	4.2%
15	1316	4.2%
14	1316	4.2%

Date
Date

Distinct	31592
Distinct (%)	> 99.9%
Missing	0
Missing (%)	0.0%
Memory size	247.0 KiB

Minimum	2019-01-01 00:00:00
Maximum	2022-08-09 11:00:00

Histogram

Histogram with fixed size bins (bins=50)

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	DateIndex	Previsión diaria D+1 demanda	Previsión diaria D+1 fotovoltaica	Previsión diaria D+1 eólica	Precio mercado SPOT Diario	Signo del desvío	Signo_lag_24h	Signo_lag_48h	Holiday	Year	Month	Day	Weekday	Hour	Date
0	2019-01-01 00:00:00	23753.0	0.0	3214.0	66.88	-1.0	NaN	NaN	1	2019	1	1	1	0	2019-01-01 00:00:00
1	2019-01-01 01:00:00	23018.0	0.0	3222.0	66.88	-1.0	NaN	NaN	1	2019	1	1	1	1	2019-01-01 01:00:00
2	2019-01-01 02:00:00	21808.0	0.0	3081.0	66.00	-1.0	NaN	NaN	1	2019	1	1	1	2	2019-01-01 02:00:00
3	2019-01-01 03:00:00	20635.0	0.0	3069.0	63.64	-1.0	NaN	NaN	1	2019	1	1	1	3	2019-01-01 03:00:00
4	2019-01-01 04:00:00	19824.0	0.0	2973.0	58.85	-1.0	NaN	NaN	1	2019	1	1	1	4	2019-01-01 04:00:00
5	2019-01-01 05:00:00	19544.0	0.0	2904.0	55.47	1.0	NaN	NaN	1	2019	1	1	1	5	2019-01-01 05:00:00
6	2019-01-01 06:00:00	19803.0	0.0	2857.0	56.00	1.0	NaN	NaN	1	2019	1	1	1	6	2019-01-01 06:00:00
7	2019-01-01 07:00:00	20208.0	0.0	2669.0	61.09	1.0	NaN	NaN	1	2019	1	1	1	7	2019-01-01 07:00:00
8	2019-01-01 08:00:00	20076.0	54.8	2454.0	61.01	-1.0	NaN	NaN	1	2019	1	1	1	8	2019-01-01 08:00:00
9	2019-01-01 09:00:00	20431.0	745.6	2202.0	61.00	1.0	NaN	NaN	1	2019	1	1	1	9	2019-01-01 09:00:00

Last rows

	DateIndex	Previsión diaria D+1 demanda	Previsión diaria D+1 fotovoltaica	Previsión diaria D+1 eólica	Precio mercado SPOT Diario	Signo del desvío	Signo_lag_24h	Signo_lag_48h	Year	Month	Day	Weekday	Hour	Date
31585	2022-08-09 02:00:00	23803.0	0.0	6412.0	135.57	-1.0	-1.0	1.0	2022	8	9	1	2	2022-08-09 02:00:00
31586	2022-08-09 03:00:00	23147.3	0.0	6176.0	130.00	1.0	-1.0	1.0	2022	8	9	1	3	2022-08-09 03:00:00
31587	2022-08-09 04:00:00	22818.8	0.0	5936.8	129.00	1.0	1.0	-1.0	2022	8	9	1	4	2022-08-09 04:00:00
31588	2022-08-09 05:00:00	23062.0	0.0	5801.8	131.01	1.0	1.0	-1.0	2022	8	9	1	5	2022-08-09 05:00:00
31589	2022-08-09 06:00:00	24664.3	0.2	5609.0	143.59	1.0	1.0	1.0	2022	8	9	1	6	2022-08-09 06:00:00
31590	2022-08-09 07:00:00	26012.8	281.5	5303.3	152.74	1.0	1.0	-1.0	2022	8	9	1	7	2022-08-09 07:00:00
31591	2022-08-09 08:00:00	27288.5	2516.3	4604.5	152.85	1.0	1.0	1.0	2022	8	9	1	8	2022-08-09 08:00:00
31592	2022-08-09 09:00:00	29001.0	6150.4	3798.5	145.49	1.0	1.0	-1.0	2022	8	9	1	9	2022-08-09 09:00:00
31593	2022-08-09 10:00:00	30403.0	8451.1	3253.8	140.00	1.0	1.0	1.0	2022	8	9	1	10	2022-08-09 10:00:00
31594	2022-08-09 11:00:00	31323.0	9565.2	3271.8	130.00	1.0	1.0	1.0	2022	8	9	1	11	2022-08-09 11:00:00

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Pearson's r

Spearman's ρ

Kendall's τ

Phik (φk)

Cramér's V (φc)

Missing values

Sample

First rows

Last rows